In practical applications, we should not only consider the effectiveness of LP, but also inference efficiency.
Many LP applications generally require fast retrieval of the top scoring neighbors for low-latency services [1]}, [2]}, [3]}.
For a Dot Product decoder, this retrieval can be approximated efficiently at the sublinear time complexity [4]}.
However, to the best of our knowledge, no such sublinear algorithms exist for the top scoring neighbor retrievals of the HadamardMLP decoders.
This means that for every source node, we have to iterate over all the nodes in the graph to compute the scores so as to find the top scoring neighbors for HadamardMLP, which is of linear complexity and cannot scale to large graphs.
